Dual cross-linked magnetic gelatin/carboxymethyl cellulose cryogels for enhanced Congo red adsorption: Experimental studies and machine learning modelling.

To achieve highly efficient and environmentally degradable adsorbents for Congo red (CR) removal, we synthesized a dual-network nanocomposite cryogel composed of gelatin/carboxymethyl cellulose, loaded with FeO nanoparticles. Gelatin and sodium carboxymethylcellulose were cross-linked using transglutaminase and calcium chloride, respectively. The cross-linking process enhanced the thermal stability of the composite cryogels. The CR adsorption process exhibited a better fit to the pseudo-second-order model and Langmuir model, with maximum adsorption capacity of 698.19 mg/g at pH of 7, temperature of 318 K, and initial CR concentration of 500 mg/L. Thermodynamic results indicated that the CR adsorption process was both spontaneous and endothermic. The performance of machine learning model showed that the Extreme Gradient Boosting model had the highest test determination coefficient (R = 0.9862) and the lowest root mean square error (RMSE = 10.3901 mg/g) among the 6 models. Feature importance analysis using SHapley Additive exPlanations (SHAP) revealed that the initial concentration had the greatest influence on the model's prediction of adsorption capacity. Density functional theory calculations indicated that there were active sites on the CR molecule that can undergo electrostatic interactions with the adsorbent. Thus, the synthesized cryogels demonstrate promising potential as adsorbents for dye removal from wastewater.